Detecção de nós injetores de falsa informação utilizando a comparação entre agrupamentos em redes de sensores sem fio

Abstract

One of the major challenges regarding sensors use is to ensure that the information being forwarded hasn’t been modified, even when several devices are compromised. The False Data Injection attack modifies the sensed data in Wireless Sensor Networks (WSNs). This type of attack is hard to defend against when using existing approaches, because most of the mechanisms will drop the fake report without verifying from which sensor node that false information is coming from. This work presents a Cluster-based False Data Detection (CFDD) mechanism for malicious nodes detection, based mainly on the comparison among neighborhood clusters. Simulation results show a detection efficiency higher than those of NFFS, GFFS and SEF mechanisms, being capable of identifying more than 30% of the malicious devices in a 255 nodes network, even when 75% of those are compromised.